Identification of Loss of Gene Function Phenomenon in Restorer Line and Its Mutant of Dian Type Hybrid Rice
Bibliographic record
Abstract
In this study, we carried out a classification survey on sterile plants in the F 1 population of high-quality and high-yield Dian-type japonica hybrid rice ‘Dianheyou 34’, combining with the basic agronomic traits and biological information of the backbone restorer line ‘Nan34’ and its mutant Rf1 restorer gene lost mutant, the phenomenon and cause of function loss of restorer lines were preliminarily explored. The results showed that the loss of function with natural mutation at Rf1 in ‘Nan34’ was the main factor that led to sterile plants (two years accounted for 0.26% and 0.79% respectively) in ‘Dianheyou 34’F 1 hybrid population; the seed setting rate of ‘Nan34’ wild type was no significant difference compared with its mutant, and the wild type was slightly higher than the mutant in pollen fertility, plant height, ear length and grain length, and slightly greater than the wild type in grain width. By cloning the Rf1 locus of ‘Nan34’ wild type and its mutant, we found that the mutant in ORF region lost a 574 bp long sequence compared with the wild type, which made it unable to encode some PPR proteins. The loss of this sequence may lead to the loss of recovery function. The results lay a theoretical foundation for revealing genetic variation mechanism of restorer genes in three-line hybrid rice breeding and utilization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".